MSc Financial Engineering
- 00:00:00Course Duration
- Skill level
-
$835
- 01 August 2024Admission Deadline
Programme Summary
Part |
Module Code |
Module Name |
Credits |
1 |
CFE5101 CFE5102 CFI5101 CFI5111 CFS 5105 |
Advanced Financial Econometrics and Data Analysis Financial Computing Financial Statement Analysis and Planning Advanced Corporate Financial Strategy Research Methods |
18 18 18 18 18 |
Total Credits Part I |
90 |
||
2 |
CFE5201 CFE5202 CFE5203 CFE5204 |
Advanced Asset Pricing Theory and Practice Derivative Securities Quantitative Risk Management Financial Computing (Practical) |
18 18 18 18 |
Total Credits Part II |
90 |
||
3 |
CFE5301 CFI5303 CFE5305 CFE5304 CFE 5308 |
Product Design and Engineering (Practical) Financial Modelling and Trading Rules Financial Time Series Analysis Advanced Capital Budgeting Big Data Management in Finance |
18 18 18 18 18 |
Total Credits Part III |
90 |
||
4 |
CFE5400 CFE5401 |
Dissertation Research Methodology(Practical) |
70 20 |
Total Credits Part IV |
90 |
||
Total Credits for the Programme |
360 |
COURSE SYNOPSIS
CFE 5101 Advanced Financial Econometrics and Data Analysis 18 Credits
This is a module in advanced econometric theory, providing a rigorous basis for the techniques of analysis of economic data. The emphasis is on analytical methods and tools of econometric analysis. It focuses upon likelihood methods. Maximum likelihood estimation, elements of asymptotic theory and the principles of hypothesis testing are covered. The implementation of these techniques to important special linear and nonlinear models is studied. Methods of non-parametric estimation are also analysed. The application of computer-based methods (e.g. Monte-Carlo analysis, the bootstrap) to econometric problems is discussed. The modern area of empirical likelihoods is introduced, combining the principles of non-parametrics and the likelihood, and is related to the bootstrap. The course also examines the techniques available in applied research for the analysis of micro-level data in the study of economic behaviour and policy problems. Further, it examines binary choice, multiple choice and ordered response models, limited dependent variable techniques, including censored and truncated regression, selectivity and double hurdle models and switching regression models, and associated diagnostic tests
CFI 5101 Financial Statement Analysis and Planning 18 Credits
The module examines concepts; conventions, standards, issues, the regulatory regime and, the reasons and progress towards harmonization of the preparation of Financial Statements and the Analysis of the Financial Statements prepared on these bases.
CFI 5111 Advanced Corporate Financial Strategy 18 Credits
The objective of the module is to examine advanced concepts and issues in corporate financial management. Topics to be covered include: The main building blocks of financial theory of: Efficient Markets Hypothesis, Agency Theory, Asset Price Theory [CAPM, APT], Option Pricing Theory, Portfolio Theory,
CFE 5102 Financial Computing 18 Credits
The module introduces approaches useful in practical applications of computing, namely Microsoft Excel, Matlab and C-programming. Comparisons between the approaches will be made by using simple common problems. The objective is to enable students to gain a level of competence with, and understanding of, computers and computer packages in a financial environment. At the end of the course, students will be able to analyse a wide range of problems arising in finance using a mixture of spreadsheets, Matlab and C programming. Topics include: Microsoft Excel – simple spreadsheets using in-built functions, optimization using Goal-Seek tool; finding roots using the Solver tool; data analysis; MATLAB – graphics, matrix computations, in-built functions, programming in MATLAB; ANSI C Programming – basic C programming (data types, arithmetic and mathematical functions, flow control, arrays); Functions – passing information to and from functions; Pointers – pointer arithmetic, the relationship between arrays and pointers; File handling – opening and closing files, reading from and writing files. Application focuses on derivative pricing and fixed income applications, treatment of discrete dividends, numerical methods for stochastic differential equations, random number generators, Monte-Carlo Methods for European and American options
CFS5105 Research Methods 18 Credits
The overall aim of this module is to acquaint candidates with the techniques of both quantitative and qualitative research and to discuss issues relating to research design, implementation, and data analysis. Research methods are oriented towards the collection (or conversion) of data in both numerical or qualitative form and use of numerals and statistics in the analysis of data collected. This will enable the researcher to make statistically valid generalizations and inferences about the topic of study. This module describes the types of both qualitative and quantitative methods and their advantages and shortcomings in application. Students will understand: the scope and purpose of writing a research proposal; the key components of research methods; types of research methods (e.g. descriptive, correlational, cause-comparative, experimental); methods of data collection; the advantages and challenges of using quantitative or qualitative methods; Use of statistical software to define, manipulate, explore, tabulate, and sort data; and the research topics to which the use of quantitative or qualitative research methods is most appropriate
CFE 5302 Derivative Securities 18 Credits
This module blends theory and practice that incorporates a new approach to teaching derivatives. This is an advanced course combining theory and practice of pricing and hedging derivative securities. The module emphasizes the applications of financial engineering and covers option and futures pricing theory and practice. Emphasis will be on the pricing of derivatives in continuous time, from the formulation of the pricing problem to the implementation of computational and numerical solution techniques. In addition, it introduces the arbitrage-based pricing of derivative securities, focusing on topics such as arbitrage, risk neutral valuation, the log-normal hypothesis, binomial trees, the Black-Scholes formula and applications, the Black-Scholes partial differentiation equation, and other analytical and numerical models. The objective is to develop modelling skills needed to value the full range of derivative securities from exchange-traded options to over-the-counter products including American versus European options, one-factor interest rate models, swaps, caps, floors, swaptions, and other interest rate-based derivatives and credit risk and credit derivatives. In addition, it covers the theory and practical applications of currency derivatives, as well as exotic and embedded options.
CFE 5203 Quantitative Risk Management 18 Credits
This module provides a broad theoretical and practical grasp of the latest risk management and security valuation techniques used in financial industry and corporate treasury. Although the core content is mathematical in nature, the non-technical student should be able to understand the mathematics through application. The emphasis is placed upon the use of martingale techniques for pricing risk. Topics include the basic hedging techniques used to handle equity and exchange rate risk; forwards, futures and derivative contracts and models of bond pricing and the term structure of interest rates.
CFI 5304 Structured Finance 18 Credits
The module is aimed at designing debt, equity and hybrid financing techniques in order to resolve particular issues or investor problems that cannot be resolved by conventional methods. The focus is on identifying situations that call for nonstandard corporate finance solutions, and the design and pricing of the situation-specific financing instruments. Such situations include, stress-induced financial restructuring, recapitalisations, private equity and leveraged buyouts, and arbitrage-driven hybrid notes; security issues that arise in securitisation transactions, financing with asset-backed securities; medium term notes and equity-linked structured notes; design and pricing of convertible, hybrids and mezzanine debt; structured leveraged finance; high leveraged bridge loans and interim financing; presale investments; sale leaseback transactions; complex domestic and cross-border leasing transactions; a variety of project financing structures, including programs provided by the World Bank, IFC, ADB and other governmental and multinational risk insurance lending agencies.
CFE5201 Advanced Asset Pricing Theory and Practice 18 Credits
The module introduces students to single-period asset pricing including the MVP theory, CAPM, SML and CML, with the aim of understanding the short-term view of corporations’ operations. The module also covers multi-period asset pricing (Multi-period portfolio theory, CAPM and APT), Active Frontiers, Bayesian Portfolio Theory and Indexation. Students are introduced to Stochastic Dynamic Control, which they will use to understand and solve HJB equations. Transaction Costs, Incentives, Trading and Market Frictions are also addressed at the end of the module. Topics include the mathematics behind the CAPM model, the APT model, and option pricing models; the review of the CAPT pricing formula derivation with and without a risk-free asset; the APT pricing formula with a one-factor structure and multi-factor structures; and option pricing formulas in discrete time (the Binomial Model) and continuous time (the Black-Scholes (Brownian) Model). On completion of the model, the students should be able to: find expressions for the portfolio frontier with and without a risk-free asset; derive expressions for expected asset returns in the CAPM framework; derive expressions for expected asset returns in the CAPM framework; derive expressions for expected asset return in a factor model (APT); demonstrate an understanding of risk-neutral probabilities; derive derivative prices in a Binomial Model; derive the partial differential equation that governs derivatives prices in a Brownian Model; apply put-call parity to options prices problems; apply the Black-Scholes Model to work out option prices.
CFE5202 Stochastic Analysis and Optimization in Finance 18 Credits
The objective of this module is to provide the background, basic ideas and methods of stochastic calculus and to apply these methods to financial models. The module introduces the concepts of arbitrage and risk-neutral pricing in a discrete-time setting. In addition, it provides an introduction to those aspects of partial differential equations and diffusion processes most relevant to finance, Random Walk and first-step analysis, Markov property, Martingales and Semi-martingales, Brownian Motion, Stochastic Integrals, deriving and proving Ito-Doeblin, backward and forward Kolgomorov equations, the Feynman-Kac formula, stepping times, Hull and White models, Cox-Ingersoll-Ross Model, and understanding the first and second fundamental theorems of finance. It covers fundamental techniques for portfolio optimization, pricing and hedging derivative securities and other aspects of continuous-time finance. Finally, the module will introduce and study Levy's processes both from an analytic point of view and also from a probabilistic one.
CFI 5303 Financial Modelling and Trading Rules 18 Credits
The module involves the financial use of computer software packages to model corporate finance problems such as: Operating Budgets, Capital budgeting, Decision Tree Analysis, Sensitivity Analysis, Computer Simulations, Business and Securities Valuations. In addition, the course examines the dominant technical analysis tools in the stock exchange markets, foreign exchange markets, and other financial markets. Furthermore, the course seeks to equip students with advanced skills needed to test for the weak-form, semi-strong form, and strong form versions of the Efficient Market Hypothesis (EMH), in the process of identifying opportunities for successful application of technical analysis tools. The learning process is not limited to the study and evaluation of existing technical analysis tools, but also equips students with skills to develop new skills.
CFE 5301Product Design and Engineering 18 Credits
The module provides students with the opportunity to design and engineer financial products, taking them from the fundamentals of design, from conception of an idea, prototype design, to a marketable end-product. The module is designed to encourage creativity and innovation so that the students may be more successful in the real-world. It provides the preliminary planning of complex and realistic financial engineering systems, which include the design, use and pricing of structured products, how the products are constructed and hedged and applied in live situations. Design concepts and techniques are introduced and the students’ design ability is developed in a design or feasibility study chosen to emphasize innovation and ingenuity to provide wide coverage of financial engineering topics. It also emphasizes design optimization of financial models. Topics include; application of engineering methods in financial design, analysis, and construction of financial contracts to meet the needs of enterprises; architecture and creation of structured notes, structured notes through repackaging vehicles, creating financial investments to match investors’ requirements, constructing a delta hedge, options Greeks and their applications, Delta, Gamma, Theta, Vega, Rho; Dynamic hedging; Black-Scholes model, Binomial model; Ho and Lee model Cox Ross Rubinstein model Interest rate-currency-, equity-linked notes, complex interest rate swap structures, risk management of structured products and derivatives; and reverse engineering.
CFE 5305 Financial Time Series Analysis 18 Credits
The aim of the module is to introduce the special statistical character of series of observations measured overtime, and to show how this affects modelling Time Series data. Stochastic processes are introduced, and measures of their heterogeneity and memory are investigated. Special important processes are covered, and these include Vector Auto-Regressive Integrated Moving-Average (VARIMA) models. The analysis covers both the time and frequency domains. We also analyse the effects of seasonality, comovements (such as cointegration and error-correction), spurious correlations, structural breaks and outliers
CFE 5307 Advanced Capital Budgeting 18 Credits
The module explains several methods of analysis that can help business managers in valuation of investment projects and making investment decisions using a rigorous cost/benefit analysis. Topics include: calculating the payback period and evaluating capital investments using the payback period; calculating the accounting rate of return and evaluating investments using the ARR; capital budgeting using the time value of money; capital rationing; inflation and capital budgeting; risk and capital budgeting; replacement of assets; scenario, sensitivity and break-even analysis; capital structure and capital budgeting; leverage and NPV analysis; managerial options; CAPM and capital budgeting; multi period capital budgeting. Innovations in the field of capital budgeting will also be considered.
CFE 5401 Research Methodology 20 Credits
At the end of this course, the students should be able to: • understand some basic concepts of research and its methodologies • identify appropriate research topics • select and define appropriate research problem and parameters • prepare a project proposal (to undertake a project) • organize and conduct research (advanced project) in a more appropriate manner • write a research report and thesis • write a research proposal (grants).
CFE 5400 Dissertation 70 Credits
The dissertation, which is compulsory, helps students to consolidate theoretical and practical knowledge gained in the Taught Section of the programme by completing a research project under the supervision of the Department staff and or professionals in sectors relevant to the topic being pursued.